Web Service Composition as Planning, Revisited: In Between Background Theories and Initial State Uncertainty

Thanks to recent advances, AI Planning has become the underlying technique for several applications. Amongst these, a prominent one is automated Web Service Composition (WSC). One important issue in this context has been hardly addressed so far: WSC requires dealing with background ontologies. The support for those is severely limited in current planning tools. We introduce a planning formalism that faithfully represents WSC. We show that, unsurprisingly, planning in such a formalism is very hard. We then identify an interesting special case that covers many relevant WSC scenarios, and where the semantics are simpler and easier to deal with. This opens the way to the development of effective support tools for WSC. Furthermore, we show that if one additionally limits the amount and form of outputs that can be generated, then the set of possible states becomes static, and can be modelled in terms of a standard notion of initial state uncertainty. For this, effective tools exist; these can realize scalable WSC with powerful background ontologies. In an initial experiment, we show how scaling WSC instances are comfortably solved by a tool incorporating modern planning heuristics.

Authors

• Joerg Hoffmann

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